Data-driven Approximation of Cumulative Distribution Function Using Particle Swarm Optimization based Finite Mixtures of Logistic Distribution
Автор: Rajasekharreddy Poreddy, Gopi E.S.
Журнал: International Journal of Intelligent Systems and Applications @ijisa
Статья в выпуске: 5 vol.16, 2024 года.
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This paper proposes a data-driven approximation of the Cumulative Distribution Function using the Finite Mixtures of the Cumulative Distribution Function of Logistic distribution. Since it is not possible to solve the logistic mixture model using the Maximum likelihood method, the mixture model is modeled to approximate the empirical cumulative distribution function using the computational intelligence algorithms. The Probability Density Function is obtained by differentiating the estimate of the Cumulative Distribution Function. The proposed technique estimates the Cumulative Distribution Function of different benchmark distributions. Also, the performance of the proposed technique is compared with the state-of-the-art kernel density estimator and the Gaussian Mixture Model. Experimental results on κ−μ distribution show that the proposed technique performs equally well in estimating the probability density function. In contrast, the proposed technique outperforms in estimating the cumulative distribution function. Also, it is evident from the experimental results that the proposed technique outperforms the state-of-the-art Gaussian Mixture model and kernel density estimation techniques with less training data.
Cumulative Distribution Function (CDF), Probability Density Function (PDF), Logistic Mixture Model (LMM), Particle Swarm Optimization (PSO), Extreme Learning Machine (ELM), Interior-point Method
Короткий адрес: https://sciup.org/15019515
IDR: 15019515 | DOI: 10.5815/ijisa.2024.05.02
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